Merge pull request #1970 from HydrogenSulfate/cppinfer_tipc

add Cppinfer tipc

test_tipc/README.md

@@ -35,15 +35,16 @@
 │   ├── MobileNetV3         # MobileNetV3系列模型测试配置文件目录
 │   │   ├── MobileNetV3_large_x1_0_train_infer_python.txt                                    #基础训练预测配置文件
 │   │   ├── MobileNetV3_large_x1_0_train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt  #多机多卡训练预测配置文件
-│   │   ├── MobileNetV3_large_x1_0_train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt  #多机多卡训练预测配置文件
-│   │   └── MobileNetV3_large_x1_0_paddle2onnx_infer_python.txt                              #paddle2onnx推理测试配置文件
-│   └── ResNet              # ResNet系列模型测试配置文件目录
-│       ├── ResNet50_vd_train_infer_python.txt                                        #基础训练预测配置文件
-│       ├── ResNet50_vd_train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt      #多机多卡训练预测配置文件
-│       ├── ResNet50_vd_train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt      #多机多卡训练预测配置文件
-│       ├── ResNet50_vd_train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt     #混合精度训练预测配置文件
-│       └── ResNet50_vd_paddle2onnx_infer_python.txt                                  #paddle2onnx推理测试配置文件
-|   ......
+│   │   ├── MobileNetV3_large_x1_0_train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt #混合精度训练预测配置文件
+│   │   ├── MobileNetV3_large_x1_0_paddle2onnx_infer_python.txt                              #paddle2onnx推理测试配置文件
+│   │   └── ......
+│   ├──ResNet              # ResNet系列模型测试配置文件目录
+│   │   ├── ResNet50_vd_train_infer_python.txt                                        #基础训练预测配置文件
+│   │   ├── ResNet50_vd_train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt      #多机多卡训练预测配置文件
+│   │   ├── ResNet50_vd_train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt     #混合精度训练预测配置文件
+│   │   ├── ResNet50_vd_paddle2onnx_infer_python.txt                                  #paddle2onnx推理测试配置文件
+│   │   └── ......
+│   └── ......
 ├── docs
 │   ├── guide.png
 │   └── test.png

test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:MobileNetV3_large_x1_0
+cpp_infer_type:cls
+cls_inference_model_dir:./MobileNetV3_large_x1_0_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/PP-ShiTu/PPShiTu_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

 ===========================cpp_infer_params===========================
 model_name:PPShiTu
 cpp_infer_type:shitu
-feature_inference_model_dir:./feature_inference/
-det_inference_model_dir:./det_inference
+feature_inference_model_dir:./general_PPLCNet_x2_5_lite_v1.0_infer/
+det_inference_model_dir:./picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/
 cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
 det_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
 infer_quant:False
 inference_cmd:./deploy/cpp_shitu/build/pp_shitu -c inference_drink.yaml
 use_gpu:True|False
-enable_mkldnn:True|False
-cpu_threads:1|6
+enable_mkldnn:False
+cpu_threads:1
 batch_size:1
-use_tensorrt:False|True
-precision:fp32|fp16
+use_tensorrt:False
+precision:fp32
 data_dir:./dataset/drink_dataset_v1.0
 benchmark:True
-generate_yaml_cmd:python3 test_tipc/generate_cpp_yaml.py
-transform_index_cmd:python3 deploy/cpp_shitu/tools/transform_id_map.py -c inference_drink.yaml
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
+transform_index_cmd:python3.7 deploy/cpp_shitu/tools/transform_id_map.py -c inference_drink.yaml

test_tipc/config/PPHGNet/PPHGNet_small_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:PPHGNet_small
+cpp_infer_type:cls
+cls_inference_model_dir:./PPHGNet_small_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/PPHGNet/PPHGNet_tiny_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:PPHGNet_tiny
+cpp_infer_type:cls
+cls_inference_model_dir:./PPHGNet_tiny_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/PPLCNet/PPLCNet_x0_25_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:PPLCNet_x0_25
+cpp_infer_type:cls
+cls_inference_model_dir:./PPLCNet_x0_25_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_25_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/PPLCNet/PPLCNet_x0_35_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:PPLCNet_x0_35
+cpp_infer_type:cls
+cls_inference_model_dir:./PPLCNet_x0_35_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_35_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/PPLCNet/PPLCNet_x0_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:PPLCNet_x0_5
+cpp_infer_type:cls
+cls_inference_model_dir:./PPLCNet_x0_5_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/PPLCNet/PPLCNet_x0_75_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:PPLCNet_x0_75
+cpp_infer_type:cls
+cls_inference_model_dir:./PPLCNet_x0_75_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_75_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:PPLCNet_x1_0
+cpp_infer_type:cls
+cls_inference_model_dir:./PPLCNet_x1_0_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/PPLCNet/PPLCNet_x1_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:PPLCNet_x1_5
+cpp_infer_type:cls
+cls_inference_model_dir:./PPLCNet_x1_5_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_5_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/PPLCNet/PPLCNet_x2_0_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:PPLCNet_x2_0
+cpp_infer_type:cls
+cls_inference_model_dir:./PPLCNet_x2_0_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_0_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/PPLCNet/PPLCNet_x2_5_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:PPLCNet_x2_5
+cpp_infer_type:cls
+cls_inference_model_dir:./PPLCNet_x2_5_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/PPLCNetV2/PPLCNetV2_base_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:PPLCNetV2_base
+cpp_infer_type:cls
+cls_inference_model_dir:./PPLCNetV2_base_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:ResNet50
+cpp_infer_type:cls
+cls_inference_model_dir:./ResNet50_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

 ===========================cpp_infer_params===========================
 model_name:ResNet50_vd
 cpp_infer_type:cls
-cls_inference_model_dir:./cls_inference/
+cls_inference_model_dir:./ResNet50_vd_infer/
 det_inference_model_dir:
-cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/ResNet50_vd_inference.tar
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar
 det_inference_url:
 infer_quant:False
 inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
 use_gpu:True|False
-enable_mkldnn:True|False
-cpu_threads:1|6
+enable_mkldnn:False
+cpu_threads:1
 batch_size:1
-use_tensorrt:False|True
-precision:fp32|fp16
-image_dir:./dataset/ILSVRC2012/val
-benchmark:True
-generate_yaml_cmd:python3 test_tipc/generate_cpp_yaml.py
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/config/SwinTransformer/SwinTransformer_tiny_patch4_window7_224_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:SwinTransformer_tiny_patch4_window7_224
+cpp_infer_type:cls
+cls_inference_model_dir:./SwinTransformer_tiny_patch4_window7_224_infer/
+det_inference_model_dir:
+cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar
+det_inference_url:
+infer_quant:False
+inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
+use_gpu:True|False
+enable_mkldnn:False
+cpu_threads:1
+batch_size:1
+use_tensorrt:False
+precision:fp32
+image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
+benchmark:False
+generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py

test_tipc/docs/test_inference_cpp.md

-# C++预测功能测试
+# Linux GPU/CPU C++ 推理功能测试
 
-C++预测功能测试的主程序为`test_inference_cpp.sh`，可以测试基于C++预测库的模型推理功能。
+Linux GPU/CPU C++ 推理功能测试的主程序为`test_inference_cpp.sh`，可以测试基于C++预测引擎的推理功能。
 
 ## 1. 测试结论汇总
 
-基于训练是否使用量化，进行本测试的模型可以分为`正常模型`和`量化模型`，这两类模型对应的C++预测功能汇总如下：
+- 推理相关：
 
-| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
-|  ----   |  ---- |   ----   |  :----:  |   :----:   |  :----:  |
-| 正常模型 | GPU | 1/6 | fp32/fp16 | - | - |
-| 正常模型 | CPU | 1/6 | - | fp32 | 支持 |
-| 量化模型 | GPU | 1/6 | int8 | - | - |
-| 量化模型 | CPU | 1/6 | - | int8 | 支持 |
+| 算法名称   | 模型名称  | device_CPU          | device_GPU |
+|  :----:   |  :----: |   :----:            |  :----:  |
+|  MobileNetV3   |  MobileNetV3_large_x1_0 |  支持 | 支持 |
+|  PP-ShiTu   |  PPShiTu_general_rec、PPShiTu_mainbody_det |  支持 | 支持 |
+|  PP-ShiTu   |  PPShiTu_mainbody_det |  支持 | 支持 |
+|  PPHGNet   |  PPHGNet_small |  支持 | 支持 |
+|  PPHGNet   |  PPHGNet_tiny |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x0_25 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x0_35 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x0_5 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x0_75 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x1_0 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x1_5 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x2_0 |  支持 | 支持 |
+|  PPLCNet   |  PPLCNet_x2_5 |  支持 | 支持 |
+|  PPLCNetV2   |  PPLCNetV2_base |  支持 | 支持 |
+|  ResNet   |  ResNet50 |  支持 | 支持 |
+|  ResNet   |  ResNet50_vd |  支持 | 支持 |
+|  SwinTransformer   |  SwinTransformer_tiny_patch4_window7_224 |  支持 | 支持 |
 
-## 2. 测试流程
-运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。
+## 2. 测试流程(以**ResNet50**为例)
 
-### 2.1 功能测试
-先运行`prepare.sh`准备数据和模型，然后运行`test_inference_cpp.sh`进行测试，最终在```test_tipc/output```目录下生成`cpp_infer_*.log`后缀的日志文件。
+
+<details>
+<summary><b>准备数据、准备推理模型、编译opencv、编译（下载）Paddle Inference、编译C++预测Demo（已写入prepare.sh自动执行，点击以展开详细内容或者折叠）
+</b></summary>
+
+### 2.1 准备数据和推理模型
+
+#### 2.1.1 准备数据
+
+默认使用`./deploy/images/ILSVRC2012_val_00000010.jpeg`作为测试输入图片。
+
+#### 2.1.2 准备推理模型
+
+* 如果已经训练好了模型，可以参考[模型导出](../../docs/zh_CN/inference_deployment/export_model.md)，导出`inference model`，并将导出路径设置为`./deploy/models/ResNet50_infer`，
+导出完毕后文件结构如下
 
 ```shell
-bash test_tipc/prepare.sh  test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt cpp_infer
+./deploy/models/ResNet50_infer/
+├── inference.pdmodel
+├── inference.pdiparams
+└── inference.pdiparams.info
+```
+
+### 2.2 准备环境
+
+#### 2.2.1 运行准备
+
+配置合适的编译和执行环境，其中包括编译器，cuda等一些基础库，建议安装docker环境，[参考链接](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/docker/linux-docker.html)。
 
-# 用法1:
-bash test_tipc/test_inference_cpp.sh test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
-# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
-bash test_tipc/test_inference_cpp.sh test_tipc/config/ResNet/ResNet50_vd_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt 1
+#### 2.2.2 编译opencv库
+
+* 首先需要从opencv官网上下载Linux环境下的源码，以3.4.7版本为例，下载及解压缩命令如下：
+
+```
+cd deploy/cpp
+wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
+tar -xvf 3.4.7.tar.gz
 ```
 
-运行预测指令后，在`test_tipc/output`文件夹下自动会保存运行日志，包括以下文件：
+* 编译opencv，首先设置opencv源码路径(`root_path`)以及安装路径(`install_path`)，`root_path`为下载的opencv源码路径，`install_path`为opencv的安装路径。在本例中，源码路径即为当前目录下的`opencv-3.4.7/`。
 
 ```shell
-test_tipc/output/
-|- results_cpp.log    # 运行指令状态的日志
-|- cls_cpp_infer_cpu_usemkldnn_False_threads_1_precision_fp32_batchsize_1.log  # CPU上不开启Mkldnn，线程数设置为1，测试batch_size=1条件下的预测运行日志
-|- cls_cpp_infer_cpu_usemkldnn_False_threads_6_precision_fp32_batchsize_1.log  # CPU上不开启Mkldnn，线程数设置为6，测试batch_size=1条件下的预测运行日志
-|- cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log # GPU上不开启TensorRT，测试batch_size=1的fp32精度预测日志
-|- cls_cpp_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log  # GPU上开启TensorRT，测试batch_size=1的fp16精度预测日志
-......
+cd ./opencv-3.4.7
+export root_path=$PWD
+export install_path=${root_path}/opencv3
 ```
-其中results_cpp.log中包含了每条指令的运行状态，如果运行成功会输出：
 
+* 然后在opencv源码路径下，按照下面的命令进行编译。
+
+```shell
+rm -rf build
+mkdir build
+cd build
+
+cmake .. \
+    -DCMAKE_INSTALL_PREFIX=${install_path} \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DBUILD_SHARED_LIBS=OFF \
+    -DWITH_IPP=OFF \
+    -DBUILD_IPP_IW=OFF \
+    -DWITH_LAPACK=OFF \
+    -DWITH_EIGEN=OFF \
+    -DCMAKE_INSTALL_LIBDIR=lib64 \
+    -DWITH_ZLIB=ON \
+    -DBUILD_ZLIB=ON \
+    -DWITH_JPEG=ON \
+    -DBUILD_JPEG=ON \
+    -DWITH_PNG=ON \
+    -DBUILD_PNG=ON \
+    -DWITH_TIFF=ON \
+    -DBUILD_TIFF=ON
+
+make -j
+make install
 ```
-Run successfully with command - ./deploy/cpp/build/clas_system -c inference_cls.yaml 2>&1|tee test_tipc/output/cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log
-......
+
+* `make install`完成之后，会在该文件夹下生成opencv头文件和库文件，用于后面的代码编译。
+
+以opencv3.4.7版本为例，最终在安装路径下的文件结构如下所示。**注意**：不同的opencv版本，下述的文件结构可能不同。
+
+```shell
+opencv3/
+├── bin     :可执行文件
+├── include :头文件
+├── lib64   :库文件
+└── share   :部分第三方库
+```
+
+#### 2.2.3 下载或者编译Paddle预测库
+
+* 有2种方式获取Paddle预测库，下面进行详细介绍。
+
+##### 预测库源码编译
+* 如果希望获取最新预测库特性，可以从Paddle github上克隆最新代码，源码编译预测库。
+* 可以参考[Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)的说明，从github上获取Paddle代码，然后进行编译，生成最新的预测库。使用git获取代码方法如下。
+
+```shell
+git clone https://github.com/PaddlePaddle/Paddle.git
+```
+
+* 进入Paddle目录后，使用如下命令编译。
+
+```shell
+rm -rf build
+mkdir build
+cd build
+
+cmake  .. \
+    -DWITH_CONTRIB=OFF \
+    -DWITH_MKL=ON \
+    -DWITH_MKLDNN=ON  \
+    -DWITH_TESTING=OFF \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DWITH_INFERENCE_API_TEST=OFF \
+    -DON_INFER=ON \
+    -DWITH_PYTHON=ON
+make -j
+make inference_lib_dist
 ```
-如果运行失败，会输出：
+
+更多编译参数选项可以参考Paddle C++预测库官网：[https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)。
+
+
+* 编译完成之后，可以在`build/paddle_inference_install_dir/`文件下看到生成了以下文件及文件夹。
+
 ```
-Run failed with command - ./deploy/cpp/build/clas_system -c inference_cls.yaml 2>&1|tee test_tipc/output/cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log
-......
+build/paddle_inference_install_dir/
+├── CMakeCache.txt
+├── paddle
+├── third_party
+└── version.txt
 ```
-可以很方便的根据results_cpp.log中的内容判定哪一个指令运行错误。
 
+其中`paddle`就是之后进行C++预测时所需的Paddle库，`version.txt`中包含当前预测库的版本信息。
+
+##### 直接下载安装
 
-### 2.2 精度测试
+* [Paddle预测库官网](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html)上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本。
 
-使用compare_results.py脚本比较模型预测的结果是否符合预期，主要步骤包括：
-- 提取日志中的预测坐标；
-- 从本地文件中提取保存好的坐标结果；
-- 比较上述两个结果是否符合精度预期，误差大于设置阈值时会报错。
+  以`manylinux_cuda11.1_cudnn8.1_avx_mkl_trt7_gcc8.2`版本为例，使用下述命令下载并解压：
+
+
+```shell
+wget https://paddle-inference-lib.bj.bcebos.com/2.2.2/cxx_c/Linux/GPU/x86-64_gcc8.2_avx_mkl_cuda11.1_cudnn8.1.1_trt7.2.3.4/paddle_inference.tgz
+
+tar -xvf paddle_inference.tgz
+```
+
+最终会在当前的文件夹中生成`paddle_inference/`的子文件夹,文件内容和上述的paddle_inference_install_dir一样。
+
+
+#### 2.2.4 编译C++预测Demo
+
+* 编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。
+
+
+```shell
+# 在deploy/cpp下执行以下命令
+bash tools/build.sh
+```
+
+具体地，`tools/build.sh`中内容如下。
+
+```shell
+OPENCV_DIR=your_opencv_dir
+LIB_DIR=your_paddle_inference_dir
+CUDA_LIB_DIR=your_cuda_lib_dir
+CUDNN_LIB_DIR=your_cudnn_lib_dir
+TENSORRT_DIR=your_tensorrt_lib_dir
+
+BUILD_DIR=build
+rm -rf ${BUILD_DIR}
+mkdir ${BUILD_DIR}
+cd ${BUILD_DIR}
+cmake .. \
+    -DPADDLE_LIB=${LIB_DIR} \
+    -DWITH_MKL=ON \
+    -DDEMO_NAME=clas_system \
+    -DWITH_GPU=OFF \
+    -DWITH_STATIC_LIB=OFF \
+    -DWITH_TENSORRT=OFF \
+    -DTENSORRT_DIR=${TENSORRT_DIR} \
+    -DOPENCV_DIR=${OPENCV_DIR} \
+    -DCUDNN_LIB=${CUDNN_LIB_DIR} \
+    -DCUDA_LIB=${CUDA_LIB_DIR} \
+
+make -j
+```
+
+上述命令中，
+
+* `OPENCV_DIR`为opencv编译安装的地址（本例中需修改为`opencv-3.4.7/opencv3`文件夹的路径）；
+
+* `LIB_DIR`为下载的Paddle预测库（`paddle_inference`文件夹），或编译生成的Paddle预测库（`build/paddle_inference_install_dir`文件夹）的路径；
+
+* `CUDA_LIB_DIR`为cuda库文件地址，在docker中一般为`/usr/local/cuda/lib64`；
+
+* `CUDNN_LIB_DIR`为cudnn库文件地址，在docker中一般为`/usr/lib64`。
+
+* `TENSORRT_DIR`是tensorrt库文件地址，在dokcer中一般为`/usr/local/TensorRT-7.2.3.4/`，TensorRT需要结合GPU使用。
+
+在执行上述命令，编译完成之后，会在当前路径下生成`build`文件夹，其中生成一个名为`clas_system`的可执行文件。
+</details>
+
+* 可执行以下命令，自动完成上述准备环境中的所需内容
+```shell
+bash test_tipc/prepare.sh test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt cpp_infer
+```
+### 2.3 功能测试
+
+
+测试方法如下所示，希望测试不同的模型文件，只需更换为自己的参数配置文件，即可完成对应模型的测试。
 
-#### 使用方式
-运行命令：
 ```shell
-python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/cls_cpp_*.txt  --log_file=./test_tipc/output/cls_cpp_*.log --atol=1e-3 --rtol=1e-3
+bash test_tipc/test_inference_cpp.sh ${your_params_file}
 ```
 
-参数介绍：  
-- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下
-- log_file: 指向运行test_tipc/test_inference_cpp.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持cpp_infer_*.log格式传入
-- atol: 设置的绝对误差
-- rtol: 设置的相对误差
+以`ResNet50`的`Linux GPU/CPU C++推理测试`为例，命令如下所示。
 
-#### 运行结果
+```shell
+bash test_tipc/test_inference_cpp.sh test_tipc/config/ResNet/ResNet50_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
+```
 
-正常运行效果如下图：
-<img src="compare_cpp_right.png" width="1000">
+输出结果如下，表示命令运行成功。
 
-出现不一致结果时的运行输出：
-<img src="compare_cpp_wrong.png" width="1000">
+```shell
+Run successfully with command - ./deploy/cpp/build/clas_system -c inference_cls.yaml > ./test_tipc/output/ResNet50/cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log 2>&1!
+Run successfully with command - ./deploy/cpp/build/clas_system -c inference_cls.yaml > ./test_tipc/output/ResNet50/cls_cpp_infer_cpu_usemkldnn_False_threads_1_precision_fp32_batchsize_1.log 2>&1!
+```
 
+最终log中会打印出结果，如下所示
+```log
+You are using Paddle compiled with TensorRT, but TensorRT dynamic library is not found. Ignore this if TensorRT is not needed.
+=======Paddle Class inference config======
+Global:
+  infer_imgs: ./deploy/images/ILSVRC2012_val_00000010.jpeg
+  inference_model_dir: ./deploy/models/ResNet50_infer
+  batch_size: 1
+  use_gpu: True
+  enable_mkldnn: True
+  cpu_num_threads: 10
+  enable_benchmark: True
+  use_fp16: False
+  ir_optim: True
+  use_tensorrt: False
+  gpu_mem: 8000
+  enable_profile: False
+PreProcess:
+  transform_ops:
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 0.00392157
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        order: ""
+        channel_num: 3
+    - ToCHWImage: ~
+PostProcess:
+  main_indicator: Topk
+  Topk:
+    topk: 5
+    class_id_map_file: ./ppcls/utils/imagenet1k_label_list.txt
+  SavePreLabel:
+    save_dir: ./pre_label/
+=======End of Paddle Class inference config======
+img_file_list length: 1
+Current image path: ./deploy/images/ILSVRC2012_val_00000010.jpeg
+Current total inferen time cost: 5449.39 ms.
+    Top1: class_id: 153, score: 0.4144, label: Maltese dog, Maltese terrier, Maltese
+    Top2: class_id: 332, score: 0.3909, label: Angora, Angora rabbit
+    Top3: class_id: 229, score: 0.0514, label: Old English sheepdog, bobtail
+    Top4: class_id: 204, score: 0.0430, label: Lhasa, Lhasa apso
+    Top5: class_id: 265, score: 0.0420, label: toy poodle
 
-## 3. 更多教程
+```
+详细log位于`./test_tipc/output/ResNet50/cls_cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log`和`./test_tipc/output/ResNet50/cls_cpp_infer_cpu_usemkldnn_False_threads_1_precision_fp32_batchsize_1.log`中。
 
-本文档为功能测试用，更详细的c++预测使用教程请参考：[服务器端C++预测](../../docs/zh_CN/inference_deployment/)  
+如果运行失败，也会在终端中输出运行失败的日志信息以及对应的运行命令。可以基于该命令，分析运行失败的原因。

test_tipc/generate_cpp_yaml.py

@@ -66,6 +66,10 @@ def main():
                                                       "test_images")
         config["IndexProcess"]["index_dir"] = os.path.join(args.data_dir,
                                                            "index")
+        config["IndexProcess"]["image_root"] = os.path.join(args.data_dir,
+                                                            "gallery")
+        config["IndexProcess"]["data_file"] = os.path.join(args.data_dir,
+                                                           "drink_label.txt")
         assert args.cls_model_dir
         assert args.det_model_dir
         config["Global"]["det_inference_model_dir"] = args.det_model_dir

test_tipc/prepare.sh

@@ -12,7 +12,7 @@ dataline=$(cat ${FILENAME})
 IFS=$'\n'
 lines=(${dataline})
 
-function func_parser_key(){
+function func_parser_key() {
     strs=$1
     IFS=":"
     array=(${strs})
@@ -20,100 +20,138 @@ function func_parser_key(){
     echo ${tmp}
 }
 
-function func_parser_value(){
+function func_parser_value() {
     strs=$1
     IFS=":"
     array=(${strs})
     if [ ${#array[*]} = 2 ]; then
         echo ${array[1]}
     else
-    	IFS="|"
-    	tmp="${array[1]}:${array[2]}"
+        IFS="|"
+        tmp="${array[1]}:${array[2]}"
         echo ${tmp}
     fi
 }
 
-function func_get_url_file_name(){
+function func_get_url_file_name() {
     strs=$1
     IFS="/"
     array=(${strs})
-    tmp=${array[${#array[@]}-1]}
+    tmp=${array[${#array[@]} - 1]}
     echo ${tmp}
 }
 
 model_name=$(func_parser_value "${lines[1]}")
 
-if [ ${MODE} = "cpp_infer" ];then
-   if [[ $FILENAME == *infer_cpp_linux_gpu_cpu.txt ]];then
-	cpp_type=$(func_parser_value "${lines[2]}")
-	cls_inference_model_dir=$(func_parser_value "${lines[3]}")
-	det_inference_model_dir=$(func_parser_value "${lines[4]}")
-	cls_inference_url=$(func_parser_value "${lines[5]}")
-	det_inference_url=$(func_parser_value "${lines[6]}")
-
-	if [[ $cpp_type == "cls" ]];then
-	    eval "wget -nc $cls_inference_url"
-	    tar xf "${model_name}_inference.tar"
-	    eval "mv inference $cls_inference_model_dir"
-	    cd dataset
-    	    rm -rf ILSVRC2012
-    	    wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_infer.tar
-    	    tar xf whole_chain_infer.tar
-    	    ln -s whole_chain_infer ILSVRC2012
-	    cd ..
-	elif [[ $cpp_type == "shitu" ]];then
-	    eval "wget -nc $cls_inference_url"
-	    tar_name=$(func_get_url_file_name "$cls_inference_url")
-	    model_dir=${tar_name%.*}
-	    eval "tar xf ${tar_name}"
-	    eval "mv ${model_dir} ${cls_inference_model_dir}"
-
-	    eval "wget -nc $det_inference_url"
-	    tar_name=$(func_get_url_file_name "$det_inference_url")
-	    model_dir=${tar_name%.*}
-	    eval "tar xf ${tar_name}"
-	    eval "mv ${model_dir} ${det_inference_model_dir}"
-	    cd dataset
-	    wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
-	    tar -xf drink_dataset_v1.0.tar
-	else
-	    echo "Wrong cpp type in config file in line 3. only support cls, shitu"
-	fi
-	exit 0
-   else
-	echo "use wrong config file"
-	exit 1
-   fi
+if [ ${MODE} = "cpp_infer" ]; then
+    if [ -d "./deploy/cpp/opencv-3.4.7/opencv3/" ] && [ $(md5sum ./deploy/cpp/opencv-3.4.7.tar.gz | awk -F ' ' '{print $1}') = "faa2b5950f8bee3f03118e600c74746a" ]; then
+        echo "################### build opencv skipped ###################"
+    else
+        echo "################### build opencv ###################"
+        rm -rf ./deploy/cpp/opencv-3.4.7.tar.gz ./deploy/cpp/opencv-3.4.7/
+        pushd ./deploy/cpp/
+        wget -nc https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/opencv-3.4.7.tar.gz
+        tar -xf opencv-3.4.7.tar.gz
+
+        cd opencv-3.4.7/
+        install_path=$(pwd)/opencv3
+        rm -rf build
+        mkdir build
+        cd build
+
+        cmake .. \
+        -DCMAKE_INSTALL_PREFIX=${install_path} \
+        -DCMAKE_BUILD_TYPE=Release \
+        -DBUILD_SHARED_LIBS=OFF \
+        -DWITH_IPP=OFF \
+        -DBUILD_IPP_IW=OFF \
+        -DWITH_LAPACK=OFF \
+        -DWITH_EIGEN=OFF \
+        -DCMAKE_INSTALL_LIBDIR=lib64 \
+        -DWITH_ZLIB=ON \
+        -DBUILD_ZLIB=ON \
+        -DWITH_JPEG=ON \
+        -DBUILD_JPEG=ON \
+        -DWITH_PNG=ON \
+        -DBUILD_PNG=ON \
+        -DWITH_TIFF=ON \
+        -DBUILD_TIFF=ON
+
+        make -j
+        make install
+        cd ../../
+        popd
+        echo "################### build opencv finished ###################"
+    fi
+    if [[ $FILENAME == *infer_cpp_linux_gpu_cpu.txt ]]; then
+        cpp_type=$(func_parser_value "${lines[2]}")
+        cls_inference_model_dir=$(func_parser_value "${lines[3]}")
+        det_inference_model_dir=$(func_parser_value "${lines[4]}")
+        cls_inference_url=$(func_parser_value "${lines[5]}")
+        det_inference_url=$(func_parser_value "${lines[6]}")
+
+        if [[ $cpp_type == "cls" ]]; then
+            eval "wget -nc $cls_inference_url"
+            tar xf "${model_name}_infer.tar"
+
+            cd dataset
+            rm -rf ILSVRC2012
+            wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_infer.tar
+            tar xf whole_chain_infer.tar
+            ln -s whole_chain_infer ILSVRC2012
+            cd ..
+        elif [[ $cpp_type == "shitu" ]]; then
+            eval "wget -nc $cls_inference_url"
+            tar_name=$(func_get_url_file_name "$cls_inference_url")
+            model_dir=${tar_name%.*}
+            eval "tar xf ${tar_name}"
+
+            eval "wget -nc $det_inference_url"
+            tar_name=$(func_get_url_file_name "$det_inference_url")
+            model_dir=${tar_name%.*}
+            eval "tar xf ${tar_name}"
+
+            cd dataset
+            wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
+            tar -xf drink_dataset_v1.0.tar
+        else
+            echo "Wrong cpp type in config file in line 3. only support cls, shitu"
+        fi
+        exit 0
+    else
+        echo "use wrong config file"
+        exit 1
+    fi
 fi
 
 model_name=$(func_parser_value "${lines[1]}")
 model_url_value=$(func_parser_value "${lines[35]}")
 model_url_key=$(func_parser_key "${lines[35]}")
 
-if [[ $FILENAME == *GeneralRecognition* ]];then
-   cd dataset
-   rm -rf Aliproduct
-   rm -rf train_reg_all_data.txt
-   rm -rf demo_train
-   wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/tipc_shitu_demo_data.tar
-   tar -xf tipc_shitu_demo_data.tar
-   ln -s tipc_shitu_demo_data Aliproduct
-   ln -s tipc_shitu_demo_data/demo_train.txt train_reg_all_data.txt
-   ln -s tipc_shitu_demo_data/demo_train demo_train
-   cd tipc_shitu_demo_data
-   ln -s demo_test.txt val_list.txt
-   cd ../../
-   eval "wget -nc $model_url_value"
-   mv general_PPLCNet_x2_5_pretrained_v1.0.pdparams GeneralRecognition_PPLCNet_x2_5_pretrained.pdparams
-   exit 0
+if [[ $FILENAME == *GeneralRecognition* ]]; then
+    cd dataset
+    rm -rf Aliproduct
+    rm -rf train_reg_all_data.txt
+    rm -rf demo_train
+    wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/tipc_shitu_demo_data.tar
+    tar -xf tipc_shitu_demo_data.tar
+    ln -s tipc_shitu_demo_data Aliproduct
+    ln -s tipc_shitu_demo_data/demo_train.txt train_reg_all_data.txt
+    ln -s tipc_shitu_demo_data/demo_train demo_train
+    cd tipc_shitu_demo_data
+    ln -s demo_test.txt val_list.txt
+    cd ../../
+    eval "wget -nc $model_url_value"
+    mv general_PPLCNet_x2_5_pretrained_v1.0.pdparams GeneralRecognition_PPLCNet_x2_5_pretrained.pdparams
+    exit 0
 fi
 
-if [[ $FILENAME == *use_dali* ]];then
+if [[ $FILENAME == *use_dali* ]]; then
     python_name=$(func_parser_value "${lines[2]}")
     ${python_name} -m pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/nightly --upgrade nvidia-dali-nightly-cuda102
 fi
 
-if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "lite_train_whole_infer" ];then
+if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "lite_train_whole_infer" ]; then
     # pretrain lite train data
     cd dataset
     rm -rf ILSVRC2012
@@ -125,7 +163,7 @@ if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "lite_train_whole_infer"
     mv val.txt val_list.txt
     cp -r train/* val/
     cd ../../
-elif [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ];then
+elif [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ]; then
     # download data
     cd dataset
     rm -rf ILSVRC2012
@@ -139,15 +177,15 @@ elif [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ];then
     # download inference or pretrained model
     eval "wget -nc $model_url_value"
     if [[ $model_url_key == *inference* ]]; then
-	rm -rf inference
-	tar xf "${model_name}_inference.tar"
+        rm -rf inference
+        tar xf "${model_name}_infer.tar"
     fi
-    if [[ $model_name == "SwinTransformer_large_patch4_window7_224" || $model_name == "SwinTransformer_large_patch4_window12_384" ]];then
-	cmd="mv ${model_name}_22kto1k_pretrained.pdparams ${model_name}_pretrained.pdparams"
-	eval $cmd
+    if [[ $model_name == "SwinTransformer_large_patch4_window7_224" || $model_name == "SwinTransformer_large_patch4_window12_384" ]]; then
+        cmd="mv ${model_name}_22kto1k_pretrained.pdparams ${model_name}_pretrained.pdparams"
+        eval $cmd
     fi
 
-elif [ ${MODE} = "whole_train_whole_infer" ];then
+elif [ ${MODE} = "whole_train_whole_infer" ]; then
     cd dataset
     rm -rf ILSVRC2012
     wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_CIFAR100.tar
@@ -159,7 +197,7 @@ elif [ ${MODE} = "whole_train_whole_infer" ];then
     cd ../../
 fi
 
-if [ ${MODE} = "serving_infer" ];then
+if [ ${MODE} = "serving_infer" ]; then
     # prepare serving env
     python_name=$(func_parser_value "${lines[2]}")
     ${python_name} -m pip install install paddle-serving-server-gpu==0.7.0.post102
@@ -187,7 +225,7 @@ if [ ${MODE} = "serving_infer" ];then
     unset https_proxy
 fi
 
-if [ ${MODE} = "paddle2onnx_infer" ];then
+if [ ${MODE} = "paddle2onnx_infer" ]; then
     # prepare paddle2onnx env
     python_name=$(func_parser_value "${lines[2]}")
     inference_model_url=$(func_parser_value "${lines[10]}")
@@ -201,11 +239,9 @@ if [ ${MODE} = "paddle2onnx_infer" ];then
     wget -nc ${inference_model_url}
     tar xf ${tar_name}
     cd ../../
-
-
 fi
 
-if [ ${MODE} = "benchmark_train" ];then
+if [ ${MODE} = "benchmark_train" ]; then
     pip install -r requirements.txt
     cd dataset
     rm -rf ILSVRC2012
@@ -213,6 +249,6 @@ if [ ${MODE} = "benchmark_train" ];then
     tar xf ILSVRC2012_val.tar
     ln -s ILSVRC2012_val ILSVRC2012
     cd ILSVRC2012
-    ln -s val_list.txt  train_list.txt
+    ln -s val_list.txt train_list.txt
     cd ../../
 fi

test_tipc/test_inference_cpp.sh

@@ -6,19 +6,18 @@ GPUID=$2
 if [[ ! $GPUID ]];then
    GPUID=0
 fi
-dataline=$(awk 'NR==1, NR==16{print}'  $FILENAME)
+dataline=$(awk 'NR==1, NR==19{print}'  $FILENAME)
 
 # parser params
 IFS=$'\n'
 lines=(${dataline})
-
-# parser cpp inference model 
+# parser cpp inference model
 model_name=$(func_parser_value "${lines[1]}")
 cpp_infer_type=$(func_parser_value "${lines[2]}")
 cpp_infer_model_dir=$(func_parser_value "${lines[3]}")
 cpp_det_infer_model_dir=$(func_parser_value "${lines[4]}")
 cpp_infer_is_quant=$(func_parser_value "${lines[7]}")
-# parser cpp inference 
+# parser cpp inference
 inference_cmd=$(func_parser_value "${lines[8]}")
 cpp_use_gpu_list=$(func_parser_value "${lines[9]}")
 cpp_use_mkldnn_list=$(func_parser_value "${lines[10]}")
@@ -31,7 +30,7 @@ cpp_benchmark_value=$(func_parser_value "${lines[16]}")
 generate_yaml_cmd=$(func_parser_value "${lines[17]}")
 transform_index_cmd=$(func_parser_value "${lines[18]}")
 
-LOG_PATH="./test_tipc/output"
+LOG_PATH="./test_tipc/output/${model_name}"
 mkdir -p ${LOG_PATH}
 status_log="${LOG_PATH}/results_cpp.log"
 # generate_yaml_cmd="python3 test_tipc/generate_cpp_yaml.py"
@@ -43,7 +42,7 @@ function func_shitu_cpp_inference(){
     _log_path=$3
     _img_dir=$4
     _flag_quant=$5
-    # inference 
+    # inference
 
     for use_gpu in ${cpp_use_gpu_list[*]}; do
         if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
@@ -58,12 +57,11 @@ function func_shitu_cpp_inference(){
                             precison="int8"
                         fi
                         _save_log_path="${_log_path}/shitu_cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
-
-			command="${generate_yaml_cmd} --type shitu --batch_size ${batch_size} --mkldnn ${use_mkldnn} --gpu ${use_gpu} --cpu_thread ${threads} --tensorrt False --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --det_model_dir ${cpp_det_infer_model_dir} --gpu_id ${GPUID}"
-			eval $command
-			eval $transform_index_cmd
-			command="${_script} 2>&1|tee ${_save_log_path}"
-			eval $command
+                        eval $transform_index_cmd
+                        command="${generate_yaml_cmd} --type shitu --batch_size ${batch_size} --mkldnn ${use_mkldnn} --gpu ${use_gpu} --cpu_thread ${threads} --tensorrt False --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --det_model_dir ${cpp_det_infer_model_dir} --gpu_id ${GPUID}"
+                        eval $command
+                        command="${_script} > ${_save_log_path} 2>&1"
+                        eval $command
                         last_status=${PIPESTATUS[0]}
                         status_check $last_status "${command}" "${status_log}"
                     done
@@ -74,7 +72,7 @@ function func_shitu_cpp_inference(){
                 for precision in ${cpp_precision_list[*]}; do
                     if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
                         continue
-                    fi 
+                    fi
                     if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
                         continue
                     fi
@@ -83,13 +81,13 @@ function func_shitu_cpp_inference(){
                     fi
                     for batch_size in ${cpp_batch_size_list[*]}; do
                         _save_log_path="${_log_path}/shitu_cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
-			command="${generate_yaml_cmd} --type shitu --batch_size ${batch_size} --mkldnn False --gpu ${use_gpu} --cpu_thread 1 --tensorrt ${use_trt} --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --det_model_dir ${cpp_det_infer_model_dir} --gpu_id ${GPUID}"
+                        eval $transform_index_cmd
+                        command="${generate_yaml_cmd} --type shitu --batch_size ${batch_size} --mkldnn False --gpu ${use_gpu} --cpu_thread 1 --tensorrt ${use_trt} --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --det_model_dir ${cpp_det_infer_model_dir} --gpu_id ${GPUID}"
+                        eval $command
+                        command="${_script} > ${_save_log_path} 2>&1"
                         eval $command
-			eval $transform_index_cmd
-			command="${_script} 2>&1|tee ${_save_log_path}"
-			eval $command
                         last_status=${PIPESTATUS[0]}
-                        status_check $last_status "${_script}" "${status_log}"
+                        status_check $last_status "${command}" "${status_log}"
                     done
                 done
             done
@@ -106,7 +104,7 @@ function func_cls_cpp_inference(){
     _log_path=$3
     _img_dir=$4
     _flag_quant=$5
-    # inference 
+    # inference
 
     for use_gpu in ${cpp_use_gpu_list[*]}; do
         if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
@@ -122,10 +120,10 @@ function func_cls_cpp_inference(){
                         fi
                         _save_log_path="${_log_path}/cls_cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
 
-			command="${generate_yaml_cmd} --type cls --batch_size ${batch_size} --mkldnn ${use_mkldnn} --gpu ${use_gpu} --cpu_thread ${threads} --tensorrt False --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --gpu_id ${GPUID}"
-			eval $command
-			command1="${_script} 2>&1|tee ${_save_log_path}"
-			eval ${command1}
+                        command="${generate_yaml_cmd} --type cls --batch_size ${batch_size} --mkldnn ${use_mkldnn} --gpu ${use_gpu} --cpu_thread ${threads} --tensorrt False --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --gpu_id ${GPUID}"
+                        eval $command
+                        command1="${_script} > ${_save_log_path} 2>&1"
+                        eval ${command1}
                         last_status=${PIPESTATUS[0]}
                         status_check $last_status "${command1}" "${status_log}"
                     done
@@ -136,7 +134,7 @@ function func_cls_cpp_inference(){
                 for precision in ${cpp_precision_list[*]}; do
                     if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
                         continue
-                    fi 
+                    fi
                     if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
                         continue
                     fi
@@ -145,10 +143,10 @@ function func_cls_cpp_inference(){
                     fi
                     for batch_size in ${cpp_batch_size_list[*]}; do
                         _save_log_path="${_log_path}/cls_cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
-			command="${generate_yaml_cmd} --type cls --batch_size ${batch_size} --mkldnn False --gpu ${use_gpu} --cpu_thread 1 --tensorrt ${use_trt} --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --gpu_id ${GPUID}"
+                        command="${generate_yaml_cmd} --type cls --batch_size ${batch_size} --mkldnn False --gpu ${use_gpu} --cpu_thread 1 --tensorrt ${use_trt} --precision ${precision} --data_dir ${_img_dir} --benchmark True --cls_model_dir ${cpp_infer_model_dir} --gpu_id ${GPUID}"
+                        eval $command
+                        command="${_script} > ${_save_log_path} 2>&1"
                         eval $command
-			command="${_script} 2>&1|tee ${_save_log_path}"
-			eval $command
                         last_status=${PIPESTATUS[0]}
                         status_check $last_status "${command}" "${status_log}"
                     done
@@ -195,49 +193,11 @@ if [[ $cpp_infer_type == "shitu" ]]; then
     cd ..
 fi
 
-if [ -d "opencv-3.4.7/opencv3/" ] && [ $(md5sum opencv-3.4.7.tar.gz | awk -F ' ' '{print $1}') = "faa2b5950f8bee3f03118e600c74746a" ];then
-    echo "################### build opencv skipped ###################"
-else
-    echo "################### build opencv ###################"
-    rm -rf opencv-3.4.7.tar.gz opencv-3.4.7/
-    wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/opencv-3.4.7.tar.gz
-    tar -xf opencv-3.4.7.tar.gz
-
-    cd opencv-3.4.7/
-    install_path=$(pwd)/opencv3
-
-    rm -rf build
-    mkdir build
-    cd build
-
-    cmake .. \
-            -DCMAKE_INSTALL_PREFIX=${install_path} \
-            -DCMAKE_BUILD_TYPE=Release \
-            -DBUILD_SHARED_LIBS=OFF \
-            -DWITH_IPP=OFF \
-            -DBUILD_IPP_IW=OFF \
-            -DWITH_LAPACK=OFF \
-            -DWITH_EIGEN=OFF \
-            -DCMAKE_INSTALL_LIBDIR=lib64 \
-            -DWITH_ZLIB=ON \
-            -DBUILD_ZLIB=ON \
-            -DWITH_JPEG=ON \
-            -DBUILD_JPEG=ON \
-            -DWITH_PNG=ON \
-            -DBUILD_PNG=ON \
-            -DWITH_TIFF=ON \
-            -DBUILD_TIFF=ON
-
-    make -j
-    make install
-    cd ../../
-    echo "################### build opencv finished ###################"
-fi
-
 echo "################### build PaddleClas demo ####################"
-OPENCV_DIR=$(pwd)/opencv-3.4.7/opencv3/
-# LIB_DIR=/work/project/project/test/paddle_inference/
-LIB_DIR=$(pwd)/Paddle/build/paddle_inference_install_dir/
+# pwd = /workspace/hesensen/PaddleClas/deploy/cpp_shitu
+OPENCV_DIR=$(dirname $PWD)/cpp/opencv-3.4.7/opencv3/
+LIB_DIR=$(dirname $PWD)/cpp/paddle_inference/
+
 CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`)
 CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`)
 
@@ -295,9 +255,9 @@ for infer_model in ${cpp_infer_model_dir[*]}; do
     #run inference
     is_quant=${infer_quant_flag[Count]}
     if [[ $cpp_infer_type == "cls" ]]; then
-	func_cls_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_image_dir_value}" ${is_quant}
+        func_cls_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_image_dir_value}" ${is_quant}
     else
-	func_shitu_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_image_dir_value}" ${is_quant}
+        func_shitu_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_image_dir_value}" ${is_quant}
     fi
     Count=$(($Count + 1))
 done